AI development

This text explains Gemini API's Function Calling, enabling the model to interact with external tools. Instead of only generating text, the model identifies needed functions, provides parameters, and receives results to formulate responses. This allows for real-time information retrieval, action execution on the user's behalf, and private data access. A four-step process is detailed: defining tools via function declarations, user requests, model function calls, and application execution of the function and returning results to the model. A Python example demonstrates building an e-commerce tool using `google.generativeai`, retrieving product price and stock. Best practices include descriptive function declarations, strong typing, error handling, result filtering/summarization, and multi-turn conversation management.

This guide details advanced Gemini API features beyond basic text input/output. It covers multimodal prompts (using text, images, video, audio), function calling (connecting Gemini to external tools and APIs), building stateful chat applications, and utilizing Gemini's "Deep Research" capabilities for analyzing large datasets and generating comprehensive reports. These features enable the creation of sophisticated and interactive applications leveraging Gemini's powerful reasoning abilities.

Welcome to the first part of our Gemini tutorial series! Before you can start building amazing things with Gemini, you need to set up your development environment. This guide will walk you through the essential steps: getting your API key and installing the necessary SDKs for Python, JavaScript, Go, and Java.

Welcome to Building with Gemini: A Developer's Guide, a comprehensive tutorial series designed to help you harness the power of Google's most capable AI models. Whether you're a seasoned developer or just starting your journey into the world of artificial intelligence, this series will provide you with the practical, step-by-step knowledge you need to integrate Gemini into your projects.